Probabilistic Machine Learning

This non-fiction guide to machine learning uses probabilistic modeling and Bayesian decision theory as its unifying lens. It offers a rigorous, up-to-date introduction that blends math, intuition, and hands-on practice for readers with an interest in data-driven thinking. The tone is clear, thoughtful, and encouraging, helping learners build confidence as they navigate complex concepts. The content is presented as a structured, concept-first treatment that moves from mathematical foundations to practical applications. Readers encounter detailed explanations, worked examples, and real-world perspectives that make abstract ideas feel tangible. A strong emphasis on reproducible practice, with online Python code and browser-based notebooks, sets this text apart and makes learning active and engaging. In addition to core theory, the book covers essential techniques and modern developments in a coherent progression. Topics include linear and logistic regression, deep neural networks, transfer learning, and unsupervised learning, all framed within probabilistic thinking. End-of-chapter exercises reinforce understanding, while an appendix of notation provides quick reference and clarity throughout the journey. - Key content elements: probabilistic modeling, Bayesian decision theory, linear and logistic regression, deep learning foundations, transfer learning, unsupervised learning, mat